{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3.1.1 单品种各龄段预测的Leslie模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "from matplotlib import pyplot as plt  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "I = np.array([0,0,2/30,3/30,5/30,7/30,8/30,1,1])\n",
    "r = 100\n",
    "beta = [1,.4,.5,.5,.5,.7,.9,.9]\n",
    "KM = 500\n",
    "init = [10*(.5**i) for i in range(9)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def K(P):\n",
    "    return I @ P\n",
    "\n",
    "def F(k):\n",
    "    return r*(1-k/KM)\n",
    "\n",
    "def S(x,k):\n",
    "    return beta[x]*(1 if x < 3 else (1-k/KM))\n",
    "\n",
    "def L(P):\n",
    "    k = K(P)\n",
    "    A = np.zeros((9,9))\n",
    "    for i in range(8):\n",
    "        A[i+1,i] = S(i,k)\n",
    "    A[0,8] = F(k)\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               1            2           3           4          5          6  \\\n0      10.000000     5.000000    2.500000    1.250000   0.625000   0.312500   \n1       3.901347    10.000000    2.000000    1.250000   0.624215   0.312108   \n2       7.012535     3.901347    4.000000    1.000000   0.624120   0.311668   \n3      12.599232     7.012535    1.560539    2.000000   0.499076   0.311483   \n4      17.607516    12.599232    2.805014    0.780269   0.998219   0.249094   \n..           ...          ...         ...         ...        ...        ...   \n196  1063.787223   835.500053  249.352309  105.551103  45.143981  24.768910   \n197   658.511013  1063.787223  334.200021  124.676154  44.702060  19.118975   \n198  1638.500596   658.511013  425.514889  167.100011  52.624806  18.868381   \n199   507.552707  1638.500596  263.404405  212.757445  70.442405  22.184427   \n200   718.371626   507.552707  655.400238  131.702203  90.259815  29.884352   \n\n             7          8          9  \n0     0.156250   0.078125   0.039062  \n1     0.218475   0.140448   0.070224  \n2     0.218168   0.196351   0.126226  \n3     0.217765   0.195988   0.176389  \n4     0.217650   0.195639   0.176075  \n..         ...        ...        ...  \n196  10.393853  25.460811   7.774425  \n197  14.685870   7.923441  19.409283  \n198  11.297953  11.157819   6.019958  \n199  11.135773   8.572943   8.466609  \n200  13.176071   8.503595   6.546545  \n\n[201 rows x 9 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>2.500000</td>\n      <td>1.250000</td>\n      <td>0.625000</td>\n      <td>0.312500</td>\n      <td>0.156250</td>\n      <td>0.078125</td>\n      <td>0.039062</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.901347</td>\n      <td>10.000000</td>\n      <td>2.000000</td>\n      <td>1.250000</td>\n      <td>0.624215</td>\n      <td>0.312108</td>\n      <td>0.218475</td>\n      <td>0.140448</td>\n      <td>0.070224</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.012535</td>\n      <td>3.901347</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>0.624120</td>\n      <td>0.311668</td>\n      <td>0.218168</td>\n      <td>0.196351</td>\n      <td>0.126226</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>12.599232</td>\n      <td>7.012535</td>\n      <td>1.560539</td>\n      <td>2.000000</td>\n      <td>0.499076</td>\n      <td>0.311483</td>\n      <td>0.217765</td>\n      <td>0.195988</td>\n      <td>0.176389</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17.607516</td>\n      <td>12.599232</td>\n      <td>2.805014</td>\n      <td>0.780269</td>\n      <td>0.998219</td>\n      <td>0.249094</td>\n      <td>0.217650</td>\n      <td>0.195639</td>\n      <td>0.176075</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>1063.787223</td>\n      <td>835.500053</td>\n      <td>249.352309</td>\n      <td>105.551103</td>\n      <td>45.143981</td>\n      <td>24.768910</td>\n      <td>10.393853</td>\n      <td>25.460811</td>\n      <td>7.774425</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>658.511013</td>\n      <td>1063.787223</td>\n      <td>334.200021</td>\n      <td>124.676154</td>\n      <td>44.702060</td>\n      <td>19.118975</td>\n      <td>14.685870</td>\n      <td>7.923441</td>\n      <td>19.409283</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>1638.500596</td>\n      <td>658.511013</td>\n      <td>425.514889</td>\n      <td>167.100011</td>\n      <td>52.624806</td>\n      <td>18.868381</td>\n      <td>11.297953</td>\n      <td>11.157819</td>\n      <td>6.019958</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>507.552707</td>\n      <td>1638.500596</td>\n      <td>263.404405</td>\n      <td>212.757445</td>\n      <td>70.442405</td>\n      <td>22.184427</td>\n      <td>11.135773</td>\n      <td>8.572943</td>\n      <td>8.466609</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>718.371626</td>\n      <td>507.552707</td>\n      <td>655.400238</td>\n      <td>131.702203</td>\n      <td>90.259815</td>\n      <td>29.884352</td>\n      <td>13.176071</td>\n      <td>8.503595</td>\n      <td>6.546545</td>\n    </tr>\n  </tbody>\n</table>\n<p>201 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "df = pd.DataFrame(columns=[*range(1,10)])\n",
    "df.loc[0] = init\n",
    "for i in range(200):\n",
    "    P = df.loc[len(df)-1]\n",
    "    P = L(P) @ P\n",
    "    df.loc[len(df)] = P\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               1            2           3           4          5          6  \\\n0      10.000000     5.000000    2.500000    1.250000   0.625000   0.312500   \n1       3.901347    10.000000    2.000000    1.250000   0.624215   0.312108   \n2       7.012535     3.901347    4.000000    1.000000   0.624120   0.311668   \n3      12.599232     7.012535    1.560539    2.000000   0.499076   0.311483   \n4      17.607516    12.599232    2.805014    0.780269   0.998219   0.249094   \n..           ...          ...         ...         ...        ...        ...   \n196  1063.787223   835.500053  249.352309  105.551103  45.143981  24.768910   \n197   658.511013  1063.787223  334.200021  124.676154  44.702060  19.118975   \n198  1638.500596   658.511013  425.514889  167.100011  52.624806  18.868381   \n199   507.552707  1638.500596  263.404405  212.757445  70.442405  22.184427   \n200   718.371626   507.552707  655.400238  131.702203  90.259815  29.884352   \n\n             7          8          9          egg  junior larvae  \\\n0     0.156250   0.078125   0.039062    15.000000       4.375000   \n1     0.218475   0.140448   0.070224    13.901347       3.874215   \n2     0.218168   0.196351   0.126226    10.913882       5.624120   \n3     0.217765   0.195988   0.176389    19.611767       4.059615   \n4     0.217650   0.195639   0.176075    30.206748       4.583503   \n..         ...        ...        ...          ...            ...   \n196  10.393853  25.460811   7.774425  1899.287276     400.047393   \n197  14.685870   7.923441  19.409283  1722.298236     503.578236   \n198  11.297953  11.157819   6.019958  2297.011608     645.239706   \n199  11.135773   8.572943   8.466609  2146.053303     546.604255   \n200  13.176071   8.503595   6.546545  1225.924333     877.362255   \n\n     senior larvae      adult  \n0         0.468750   0.117188  \n1         0.530583   0.210673  \n2         0.529836   0.322577  \n3         0.529248   0.372377  \n4         0.466744   0.371714  \n..             ...        ...  \n196      35.162763  33.235236  \n197      33.804845  27.332724  \n198      30.166334  17.177777  \n199      33.320200  17.039552  \n200      43.060423  15.050139  \n\n[201 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>egg</th>\n      <th>junior larvae</th>\n      <th>senior larvae</th>\n      <th>adult</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>2.500000</td>\n      <td>1.250000</td>\n      <td>0.625000</td>\n      <td>0.312500</td>\n      <td>0.156250</td>\n      <td>0.078125</td>\n      <td>0.039062</td>\n      <td>15.000000</td>\n      <td>4.375000</td>\n      <td>0.468750</td>\n      <td>0.117188</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.901347</td>\n      <td>10.000000</td>\n      <td>2.000000</td>\n      <td>1.250000</td>\n      <td>0.624215</td>\n      <td>0.312108</td>\n      <td>0.218475</td>\n      <td>0.140448</td>\n      <td>0.070224</td>\n      <td>13.901347</td>\n      <td>3.874215</td>\n      <td>0.530583</td>\n      <td>0.210673</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7.012535</td>\n      <td>3.901347</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>0.624120</td>\n      <td>0.311668</td>\n      <td>0.218168</td>\n      <td>0.196351</td>\n      <td>0.126226</td>\n      <td>10.913882</td>\n      <td>5.624120</td>\n      <td>0.529836</td>\n      <td>0.322577</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>12.599232</td>\n      <td>7.012535</td>\n      <td>1.560539</td>\n      <td>2.000000</td>\n      <td>0.499076</td>\n      <td>0.311483</td>\n      <td>0.217765</td>\n      <td>0.195988</td>\n      <td>0.176389</td>\n      <td>19.611767</td>\n      <td>4.059615</td>\n      <td>0.529248</td>\n      <td>0.372377</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17.607516</td>\n      <td>12.599232</td>\n      <td>2.805014</td>\n      <td>0.780269</td>\n      <td>0.998219</td>\n      <td>0.249094</td>\n      <td>0.217650</td>\n      <td>0.195639</td>\n      <td>0.176075</td>\n      <td>30.206748</td>\n      <td>4.583503</td>\n      <td>0.466744</td>\n      <td>0.371714</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>1063.787223</td>\n      <td>835.500053</td>\n      <td>249.352309</td>\n      <td>105.551103</td>\n      <td>45.143981</td>\n      <td>24.768910</td>\n      <td>10.393853</td>\n      <td>25.460811</td>\n      <td>7.774425</td>\n      <td>1899.287276</td>\n      <td>400.047393</td>\n      <td>35.162763</td>\n      <td>33.235236</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>658.511013</td>\n      <td>1063.787223</td>\n      <td>334.200021</td>\n      <td>124.676154</td>\n      <td>44.702060</td>\n      <td>19.118975</td>\n      <td>14.685870</td>\n      <td>7.923441</td>\n      <td>19.409283</td>\n      <td>1722.298236</td>\n      <td>503.578236</td>\n      <td>33.804845</td>\n      <td>27.332724</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>1638.500596</td>\n      <td>658.511013</td>\n      <td>425.514889</td>\n      <td>167.100011</td>\n      <td>52.624806</td>\n      <td>18.868381</td>\n      <td>11.297953</td>\n      <td>11.157819</td>\n      <td>6.019958</td>\n      <td>2297.011608</td>\n      <td>645.239706</td>\n      <td>30.166334</td>\n      <td>17.177777</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>507.552707</td>\n      <td>1638.500596</td>\n      <td>263.404405</td>\n      <td>212.757445</td>\n      <td>70.442405</td>\n      <td>22.184427</td>\n      <td>11.135773</td>\n      <td>8.572943</td>\n      <td>8.466609</td>\n      <td>2146.053303</td>\n      <td>546.604255</td>\n      <td>33.320200</td>\n      <td>17.039552</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>718.371626</td>\n      <td>507.552707</td>\n      <td>655.400238</td>\n      <td>131.702203</td>\n      <td>90.259815</td>\n      <td>29.884352</td>\n      <td>13.176071</td>\n      <td>8.503595</td>\n      <td>6.546545</td>\n      <td>1225.924333</td>\n      <td>877.362255</td>\n      <td>43.060423</td>\n      <td>15.050139</td>\n    </tr>\n  </tbody>\n</table>\n<p>201 rows × 13 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "df['egg'] = df[1] + df[2]\n",
    "df['junior larvae'] = df[3] + df[4] + df[5]\n",
    "df['senior larvae'] = df[6] + df[7]\n",
    "df['adult'] = df[8] + df[9]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x21143f760f0>"
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for age in ['egg','junior larvae','senior larvae','adult']:\n",
    "    plt.plot([*range(len(df))],df[age]*10**3,label=age)\n",
    "plt.yscale('log')\n",
    "plt.xlabel('weeks')\n",
    "plt.ylabel('amount of locusts')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python36864bit3f53e4684f024fc68f038ddcedf7a612",
   "display_name": "Python 3.6.8 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}